Overview

Dataset statistics

Number of variables21
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory164.2 KiB
Average record size in memory168.1 B

Variable types

Numeric15
Categorical6

Alerts

fc is highly overall correlated with pcHigh correlation
pc is highly overall correlated with fcHigh correlation
four_g is highly overall correlated with three_gHigh correlation
three_g is highly overall correlated with four_gHigh correlation
id is uniformly distributedUniform
touch_screen is uniformly distributedUniform
id has unique valuesUnique
fc has 210 (21.0%) zerosZeros
pc has 40 (4.0%) zerosZeros
sc_w has 112 (11.2%) zerosZeros

Reproduction

Analysis started2023-03-27 15:39:11.588600
Analysis finished2023-03-27 15:39:46.322074
Duration34.73 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:46.440999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2023-03-27T20:09:46.649445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

battery_power
Real number (ℝ)

Distinct721
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1248.51
Minimum500
Maximum1999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:46.802036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile574
Q1895
median1246.5
Q31629.25
95-th percentile1933
Maximum1999
Range1499
Interquartile range (IQR)734.25

Descriptive statistics

Standard deviation432.45823
Coefficient of variation (CV)0.34637947
Kurtosis-1.171261
Mean1248.51
Median Absolute Deviation (MAD)365
Skewness0.038771028
Sum1248510
Variance187020.12
MonotonicityNot monotonic
2023-03-27T20:09:46.930977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1074 5
 
0.5%
1981 5
 
0.5%
529 4
 
0.4%
1745 4
 
0.4%
1715 4
 
0.4%
560 4
 
0.4%
986 4
 
0.4%
723 4
 
0.4%
900 4
 
0.4%
1472 3
 
0.3%
Other values (711) 959
95.9%
ValueCountFrequency (%)
500 2
0.2%
504 1
 
0.1%
507 1
 
0.1%
510 1
 
0.1%
511 1
 
0.1%
517 1
 
0.1%
518 2
0.2%
519 3
0.3%
520 1
 
0.1%
521 1
 
0.1%
ValueCountFrequency (%)
1999 1
0.1%
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1995 1
0.1%
1992 1
0.1%
1991 1
0.1%
1989 1
0.1%
1988 2
0.2%
1986 2
0.2%

blue
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
516 
0
484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 516
51.6%
0 484
48.4%

Length

2023-03-27T20:09:47.046476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T20:09:47.144490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 516
51.6%
0 484
48.4%

Most occurring characters

ValueCountFrequency (%)
1 516
51.6%
0 484
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 516
51.6%
0 484
48.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 516
51.6%
0 484
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 516
51.6%
0 484
48.4%

clock_speed
Real number (ℝ)

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5409
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:47.279131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.3
95-th percentile2.9
Maximum3
Range2.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.82926767
Coefficient of variation (CV)0.53817099
Kurtosis-1.3403053
Mean1.5409
Median Absolute Deviation (MAD)0.8
Skewness0.18593828
Sum1540.9
Variance0.68768487
MonotonicityNot monotonic
2023-03-27T20:09:47.422746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5 199
19.9%
0.6 40
 
4.0%
2.6 40
 
4.0%
1.4 39
 
3.9%
2.9 38
 
3.8%
1.3 38
 
3.8%
2.1 37
 
3.7%
1.6 37
 
3.7%
2.5 36
 
3.6%
0.9 34
 
3.4%
Other values (16) 462
46.2%
ValueCountFrequency (%)
0.5 199
19.9%
0.6 40
 
4.0%
0.7 28
 
2.8%
0.8 29
 
2.9%
0.9 34
 
3.4%
1 26
 
2.6%
1.1 33
 
3.3%
1.2 27
 
2.7%
1.3 38
 
3.8%
1.4 39
 
3.9%
ValueCountFrequency (%)
3 21
2.1%
2.9 38
3.8%
2.8 33
3.3%
2.7 33
3.3%
2.6 40
4.0%
2.5 36
3.6%
2.4 33
3.3%
2.3 28
2.8%
2.2 29
2.9%
2.1 37
3.7%

dual_sim
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
517 
0
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Length

2023-03-27T20:09:47.544097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T20:09:47.641605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring characters

ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

fc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.593
Minimum0
Maximum19
Zeros210
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:47.725417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile14
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4633252
Coefficient of variation (CV)0.97176687
Kurtosis0.25667933
Mean4.593
Median Absolute Deviation (MAD)3
Skewness0.9895477
Sum4593
Variance19.921272
MonotonicityNot monotonic
2023-03-27T20:09:47.821630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 210
21.0%
1 124
12.4%
2 97
9.7%
4 80
 
8.0%
5 74
 
7.4%
3 70
 
7.0%
6 59
 
5.9%
7 50
 
5.0%
9 41
 
4.1%
8 38
 
3.8%
Other values (10) 157
15.7%
ValueCountFrequency (%)
0 210
21.0%
1 124
12.4%
2 97
9.7%
3 70
 
7.0%
4 80
 
8.0%
5 74
 
7.4%
6 59
 
5.9%
7 50
 
5.0%
8 38
 
3.8%
9 41
 
4.1%
ValueCountFrequency (%)
19 2
 
0.2%
18 10
 
1.0%
17 2
 
0.2%
16 11
 
1.1%
15 12
 
1.2%
14 16
1.6%
13 21
2.1%
12 17
1.7%
11 29
2.9%
10 37
3.7%

four_g
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
513 
1
487 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 513
51.3%
1 487
48.7%

Length

2023-03-27T20:09:47.955230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T20:09:48.092860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 513
51.3%
1 487
48.7%

Most occurring characters

ValueCountFrequency (%)
0 513
51.3%
1 487
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 513
51.3%
1 487
48.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 513
51.3%
1 487
48.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 513
51.3%
1 487
48.7%

int_memory
Real number (ℝ)

Distinct63
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.652
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:48.217526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q118
median34.5
Q349
95-th percentile62
Maximum64
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.128694
Coefficient of variation (CV)0.53871074
Kurtosis-1.1566454
Mean33.652
Median Absolute Deviation (MAD)15.5
Skewness-0.071781459
Sum33652
Variance328.64955
MonotonicityNot monotonic
2023-03-27T20:09:48.384082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 27
 
2.7%
38 26
 
2.6%
3 24
 
2.4%
33 24
 
2.4%
24 24
 
2.4%
40 23
 
2.3%
47 22
 
2.2%
44 22
 
2.2%
48 21
 
2.1%
63 21
 
2.1%
Other values (53) 766
76.6%
ValueCountFrequency (%)
2 14
1.4%
3 24
2.4%
4 10
1.0%
5 16
1.6%
6 13
1.3%
7 19
1.9%
8 18
1.8%
9 12
1.2%
10 18
1.8%
11 12
1.2%
ValueCountFrequency (%)
64 17
1.7%
63 21
2.1%
62 17
1.7%
61 13
1.3%
60 11
1.1%
59 11
1.1%
58 18
1.8%
57 18
1.8%
56 27
2.7%
55 15
1.5%

m_dep
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5175
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:48.517724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.5
Q30.8
95-th percentile0.9
Maximum1
Range0.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28086052
Coefficient of variation (CV)0.54272565
Kurtosis-1.2069214
Mean0.5175
Median Absolute Deviation (MAD)0.2
Skewness0.01405448
Sum517.5
Variance0.078882633
MonotonicityNot monotonic
2023-03-27T20:09:48.662337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1 139
13.9%
0.5 122
12.2%
0.9 107
10.7%
0.8 105
10.5%
0.7 101
10.1%
0.6 98
9.8%
0.4 96
9.6%
0.2 95
9.5%
0.3 88
8.8%
1 49
 
4.9%
ValueCountFrequency (%)
0.1 139
13.9%
0.2 95
9.5%
0.3 88
8.8%
0.4 96
9.6%
0.5 122
12.2%
0.6 98
9.8%
0.7 101
10.1%
0.8 105
10.5%
0.9 107
10.7%
1 49
 
4.9%
ValueCountFrequency (%)
1 49
 
4.9%
0.9 107
10.7%
0.8 105
10.5%
0.7 101
10.1%
0.6 98
9.8%
0.5 122
12.2%
0.4 96
9.6%
0.3 88
8.8%
0.2 95
9.5%
0.1 139
13.9%

mobile_wt
Real number (ℝ)

Distinct121
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.511
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:48.772081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile85
Q1109.75
median139
Q3170
95-th percentile194
Maximum200
Range120
Interquartile range (IQR)60.25

Descriptive statistics

Standard deviation34.85155
Coefficient of variation (CV)0.2498122
Kurtosis-1.1978192
Mean139.511
Median Absolute Deviation (MAD)30
Skewness0.0075312923
Sum139511
Variance1214.6305
MonotonicityNot monotonic
2023-03-27T20:09:48.940593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 17
 
1.7%
174 15
 
1.5%
128 14
 
1.4%
141 13
 
1.3%
171 13
 
1.3%
98 12
 
1.2%
119 12
 
1.2%
153 12
 
1.2%
135 12
 
1.2%
183 12
 
1.2%
Other values (111) 868
86.8%
ValueCountFrequency (%)
80 9
0.9%
81 9
0.9%
82 6
 
0.6%
83 17
1.7%
84 3
 
0.3%
85 10
1.0%
86 7
0.7%
87 11
1.1%
88 8
0.8%
89 7
0.7%
ValueCountFrequency (%)
200 5
0.5%
199 8
0.8%
198 9
0.9%
197 11
1.1%
196 9
0.9%
195 5
0.5%
194 6
0.6%
193 9
0.9%
192 7
0.7%
191 9
0.9%

n_cores
Real number (ℝ)

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.328
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:49.053437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2881546
Coefficient of variation (CV)0.52868638
Kurtosis-1.1894772
Mean4.328
Median Absolute Deviation (MAD)2
Skewness0.12256992
Sum4328
Variance5.2356517
MonotonicityNot monotonic
2023-03-27T20:09:49.136597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 142
14.2%
1 138
13.8%
2 134
13.4%
5 130
13.0%
3 127
12.7%
8 121
12.1%
7 107
10.7%
6 101
10.1%
ValueCountFrequency (%)
1 138
13.8%
2 134
13.4%
3 127
12.7%
4 142
14.2%
5 130
13.0%
6 101
10.1%
7 107
10.7%
8 121
12.1%
ValueCountFrequency (%)
8 121
12.1%
7 107
10.7%
6 101
10.1%
5 130
13.0%
4 142
14.2%
3 127
12.7%
2 134
13.4%
1 138
13.8%

pc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.054
Minimum0
Maximum20
Zeros40
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:49.239597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q316
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.0950992
Coefficient of variation (CV)0.60623624
Kurtosis-1.2408147
Mean10.054
Median Absolute Deviation (MAD)5
Skewness0.0040376552
Sum10054
Variance37.150234
MonotonicityNot monotonic
2023-03-27T20:09:49.362266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
16 59
 
5.9%
17 58
 
5.8%
1 57
 
5.7%
6 56
 
5.6%
14 55
 
5.5%
20 55
 
5.5%
7 52
 
5.2%
5 52
 
5.2%
3 51
 
5.1%
9 48
 
4.8%
Other values (11) 457
45.7%
ValueCountFrequency (%)
0 40
4.0%
1 57
5.7%
2 43
4.3%
3 51
5.1%
4 41
4.1%
5 52
5.2%
6 56
5.6%
7 52
5.2%
8 40
4.0%
9 48
4.8%
ValueCountFrequency (%)
20 55
5.5%
19 41
4.1%
18 41
4.1%
17 58
5.8%
16 59
5.9%
15 42
4.2%
14 55
5.5%
13 37
3.7%
12 46
4.6%
11 42
4.2%

px_height
Real number (ℝ)

Distinct694
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean627.121
Minimum0
Maximum1907
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:49.496386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1263.75
median564.5
Q3903
95-th percentile1424.05
Maximum1907
Range1907
Interquartile range (IQR)639.25

Descriptive statistics

Standard deviation432.9297
Coefficient of variation (CV)0.69034476
Kurtosis-0.34136306
Mean627.121
Median Absolute Deviation (MAD)321.5
Skewness0.60943187
Sum627121
Variance187428.12
MonotonicityNot monotonic
2023-03-27T20:09:49.622081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 6
 
0.6%
185 5
 
0.5%
35 5
 
0.5%
633 5
 
0.5%
346 5
 
0.5%
349 5
 
0.5%
299 4
 
0.4%
174 4
 
0.4%
1019 4
 
0.4%
406 4
 
0.4%
Other values (684) 953
95.3%
ValueCountFrequency (%)
0 2
0.2%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 3
0.3%
9 2
0.2%
10 2
0.2%
11 1
 
0.1%
ValueCountFrequency (%)
1907 1
0.1%
1900 1
0.1%
1853 1
0.1%
1842 1
0.1%
1816 2
0.2%
1813 1
0.1%
1805 1
0.1%
1788 1
0.1%
1783 1
0.1%
1768 1
0.1%

px_width
Real number (ℝ)

Distinct743
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1239.774
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:49.761710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile565.95
Q1831.75
median1250
Q31637.75
95-th percentile1905.15
Maximum1998
Range1497
Interquartile range (IQR)806

Descriptive statistics

Standard deviation439.67098
Coefficient of variation (CV)0.35463801
Kurtosis-1.257836
Mean1239.774
Median Absolute Deviation (MAD)404.5
Skewness-0.030879514
Sum1239774
Variance193310.57
MonotonicityNot monotonic
2023-03-27T20:09:49.928395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 4
 
0.4%
1521 4
 
0.4%
1443 4
 
0.4%
628 3
 
0.3%
1386 3
 
0.3%
517 3
 
0.3%
695 3
 
0.3%
610 3
 
0.3%
1896 3
 
0.3%
1151 3
 
0.3%
Other values (733) 967
96.7%
ValueCountFrequency (%)
501 1
 
0.1%
502 2
0.2%
507 2
0.2%
508 2
0.2%
512 1
 
0.1%
514 2
0.2%
516 2
0.2%
517 3
0.3%
518 3
0.3%
520 1
 
0.1%
ValueCountFrequency (%)
1998 1
 
0.1%
1997 1
 
0.1%
1996 1
 
0.1%
1993 1
 
0.1%
1989 1
 
0.1%
1986 1
 
0.1%
1985 1
 
0.1%
1984 1
 
0.1%
1981 3
0.3%
1980 1
 
0.1%

ram
Real number (ℝ)

Distinct872
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2138.998
Minimum263
Maximum3989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:50.065358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum263
5-th percentile411.7
Q11237.25
median2153.5
Q33065.5
95-th percentile3836.2
Maximum3989
Range3726
Interquartile range (IQR)1828.25

Descriptive statistics

Standard deviation1088.0923
Coefficient of variation (CV)0.50869252
Kurtosis-1.1883193
Mean2138.998
Median Absolute Deviation (MAD)913.5
Skewness-0.048188914
Sum2138998
Variance1183944.8
MonotonicityNot monotonic
2023-03-27T20:09:50.189075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1435 3
 
0.3%
3816 3
 
0.3%
1972 3
 
0.3%
3386 3
 
0.3%
2179 3
 
0.3%
1580 3
 
0.3%
625 3
 
0.3%
2585 3
 
0.3%
1715 3
 
0.3%
1895 3
 
0.3%
Other values (862) 970
97.0%
ValueCountFrequency (%)
263 1
0.1%
265 1
0.1%
267 1
0.1%
275 1
0.1%
286 1
0.1%
291 1
0.1%
292 1
0.1%
293 1
0.1%
294 1
0.1%
299 1
0.1%
ValueCountFrequency (%)
3989 1
0.1%
3984 1
0.1%
3976 1
0.1%
3975 1
0.1%
3973 1
0.1%
3969 1
0.1%
3964 1
0.1%
3959 1
0.1%
3954 1
0.1%
3953 1
0.1%

sc_h
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.995
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:50.340627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.3206067
Coefficient of variation (CV)0.36020065
Kurtosis-1.1974981
Mean11.995
Median Absolute Deviation (MAD)4
Skewness-0.038292776
Sum11995
Variance18.667643
MonotonicityNot monotonic
2023-03-27T20:09:50.446343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 77
 
7.7%
17 74
 
7.4%
13 73
 
7.3%
15 72
 
7.2%
14 70
 
7.0%
11 69
 
6.9%
9 67
 
6.7%
8 67
 
6.7%
16 65
 
6.5%
12 64
 
6.4%
Other values (5) 302
30.2%
ValueCountFrequency (%)
5 77
7.7%
6 62
6.2%
7 61
6.1%
8 67
6.7%
9 67
6.7%
10 56
5.6%
11 69
6.9%
12 64
6.4%
13 73
7.3%
14 70
7.0%
ValueCountFrequency (%)
19 64
6.4%
18 59
5.9%
17 74
7.4%
16 65
6.5%
15 72
7.2%
14 70
7.0%
13 73
7.3%
12 64
6.4%
11 69
6.9%
10 56
5.6%

sc_w
Real number (ℝ)

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.316
Minimum0
Maximum18
Zeros112
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:50.540091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2400616
Coefficient of variation (CV)0.79760376
Kurtosis-0.06930117
Mean5.316
Median Absolute Deviation (MAD)3
Skewness0.77816283
Sum5316
Variance17.978122
MonotonicityNot monotonic
2023-03-27T20:09:51.025793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 112
11.2%
2 107
10.7%
1 99
9.9%
4 95
9.5%
5 93
9.3%
3 84
8.4%
6 73
7.3%
7 67
 
6.7%
10 52
 
5.2%
9 46
 
4.6%
Other values (9) 172
17.2%
ValueCountFrequency (%)
0 112
11.2%
1 99
9.9%
2 107
10.7%
3 84
8.4%
4 95
9.5%
5 93
9.3%
6 73
7.3%
7 67
6.7%
8 44
 
4.4%
9 46
4.6%
ValueCountFrequency (%)
18 5
 
0.5%
17 8
 
0.8%
16 7
 
0.7%
15 15
 
1.5%
14 20
 
2.0%
13 23
2.3%
12 21
2.1%
11 29
2.9%
10 52
5.2%
9 46
4.6%

talk_time
Real number (ℝ)

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.085
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-27T20:09:51.130233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16.75
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation5.4976358
Coefficient of variation (CV)0.49595271
Kurtosis-1.1877259
Mean11.085
Median Absolute Deviation (MAD)5
Skewness0.015640081
Sum11085
Variance30.223999
MonotonicityNot monotonic
2023-03-27T20:09:51.225653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10 74
 
7.4%
19 66
 
6.6%
7 59
 
5.9%
20 57
 
5.7%
16 56
 
5.6%
4 55
 
5.5%
18 54
 
5.4%
12 54
 
5.4%
13 54
 
5.4%
2 51
 
5.1%
Other values (9) 420
42.0%
ValueCountFrequency (%)
2 51
5.1%
3 49
4.9%
4 55
5.5%
5 51
5.1%
6 44
4.4%
7 59
5.9%
8 50
5.0%
9 49
4.9%
10 74
7.4%
11 48
4.8%
ValueCountFrequency (%)
20 57
5.7%
19 66
6.6%
18 54
5.4%
17 37
3.7%
16 56
5.6%
15 46
4.6%
14 46
4.6%
13 54
5.4%
12 54
5.4%
11 48
4.8%

three_g
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
756 
0
244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 756
75.6%
0 244
 
24.4%

Length

2023-03-27T20:09:51.353315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T20:09:51.444116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 756
75.6%
0 244
 
24.4%

Most occurring characters

ValueCountFrequency (%)
1 756
75.6%
0 244
 
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 756
75.6%
0 244
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 756
75.6%
0 244
 
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 756
75.6%
0 244
 
24.4%

touch_screen
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
500 
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 500
50.0%
0 500
50.0%

Length

2023-03-27T20:09:51.562798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T20:09:51.670509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 500
50.0%
0 500
50.0%

Most occurring characters

ValueCountFrequency (%)
1 500
50.0%
0 500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 500
50.0%
0 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 500
50.0%
0 500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 500
50.0%
0 500
50.0%

wifi
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
507 
0
493 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Length

2023-03-27T20:09:51.747261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T20:09:51.836732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring characters

ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 507
50.7%
0 493
49.3%

Interactions

2023-03-27T20:09:43.573034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:12.808202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:14.933517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.859249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:18.873850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:21.185877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:23.553491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.563459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.554372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:29.916507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:32.065997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.153094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:36.281271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:38.914800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:41.470479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:43.708673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:12.962598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.050234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.975938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:19.033419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:21.551659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:23.684141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.732953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.693000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:30.038677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:32.224572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.272775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:36.421895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:39.070382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:41.632046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:43.860266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.086298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.181881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:17.098657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:19.210942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:21.688625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:23.842630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.861609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.836657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:30.167722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:32.386915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.468777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:36.611388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:39.190063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:41.761700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:44.023869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.204982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.309314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:17.240279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:19.384476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:21.854180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:23.972286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.979298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.960284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:30.346245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:32.505598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.590453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:36.796892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:39.385542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:41.879384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:44.148494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.328862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.435561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:17.386887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:19.541058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:21.979842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:24.098944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:26.101966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:28.088944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:30.483917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:32.631263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.720110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:36.968433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:39.508214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.001634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:44.317044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.448241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.560229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:17.502577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:19.669701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:22.129750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:24.249542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:26.238600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:28.216054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:30.605284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:32.772884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.838789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:37.155933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:39.629886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.131289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:44.447695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.562466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.680906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:17.638254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:19.851402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:22.285334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:24.369209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:26.389198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:28.335734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:30.724967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:32.933454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.952189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:37.283590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:39.812911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.288869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:44.575170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.682239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.808407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:17.775299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:20.009978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:22.410002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:24.488218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:26.505888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:28.495313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:30.899124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:33.057122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:35.082838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:37.433190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:40.355461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.408206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:44.716270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.800606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:15.936708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:17.899558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:20.138433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:22.531679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:24.666623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:26.628558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:28.630946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:31.033765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:33.181791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:35.210497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:37.718428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:40.516031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.532873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:44.882823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:13.927293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.075421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:18.028752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:20.319957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:22.690251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:24.790292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:26.757016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:28.811465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:31.163417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:33.312440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:35.399991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:37.874011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:40.653665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.668511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:45.013514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:14.056947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.208586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:18.151743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:20.456584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:22.827883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:24.910969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:26.905069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:28.952088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:31.325357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:33.444090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:35.527651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:38.074476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:40.795284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.791370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:45.160084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:14.175629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.340236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:18.274414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:20.583241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:22.950639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.049337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.059656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:29.114651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:31.504876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:33.630593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:35.655548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:38.278929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:40.976799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:42.931994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:45.343592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:14.296730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.468492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:18.410089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:20.772983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:23.074772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.201929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.180333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:29.242309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:31.660581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:33.766230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:35.804190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:38.412571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:41.097476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:43.091568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:45.479270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:14.412080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.594158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:18.575646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:20.903633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:23.218389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.320610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.302007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:29.404875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:31.789243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:33.892892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:35.940214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:38.589099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:41.212169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:43.283729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:45.608882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:14.535752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:16.728082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:18.736218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:21.061211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:23.376963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:25.442781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:27.428668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:29.772891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:31.917005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:34.020943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:36.078813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:38.734710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:41.335841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T20:09:43.409395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-27T20:09:51.931480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
idbattery_powerclock_speedfcint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timebluedual_simfour_gthree_gtouch_screenwifi
id1.000-0.0220.0340.022-0.013-0.003-0.008-0.0160.002-0.026-0.012-0.044-0.0120.0070.0310.0000.0950.0000.0000.0000.000
battery_power-0.0221.000-0.041-0.0050.004-0.010-0.0460.0270.0120.0580.051-0.032-0.054-0.0330.0150.0620.0000.0000.0000.0000.026
clock_speed0.034-0.0411.0000.020-0.0320.017-0.017-0.0110.0540.0070.067-0.001-0.030-0.018-0.0820.0000.0000.0000.0220.0290.000
fc0.022-0.0050.0201.000-0.0160.0010.0220.0200.681-0.0020.041-0.0480.0460.012-0.0490.0280.0000.0000.0170.0000.009
int_memory-0.0130.004-0.032-0.0161.000-0.003-0.0110.0230.022-0.002-0.007-0.008-0.0090.0230.0240.0000.0000.0000.0000.0000.000
m_dep-0.003-0.0100.0170.001-0.0031.000-0.0420.0090.0120.0700.0360.018-0.025-0.0110.0240.0260.0440.0000.0000.0290.065
mobile_wt-0.008-0.046-0.0170.022-0.011-0.0421.000-0.0410.0270.016-0.0150.028-0.0240.025-0.0220.0460.0000.0060.0910.0870.090
n_cores-0.0160.027-0.0110.0200.0230.009-0.0411.0000.015-0.040-0.060-0.041-0.036-0.000-0.0030.0370.0000.0000.0000.0360.000
pc0.0020.0120.0540.6810.0220.0120.0270.0151.0000.0330.056-0.0460.020-0.003-0.0380.0920.0770.0000.0000.0000.000
px_height-0.0260.0580.007-0.002-0.0020.0700.016-0.0400.0331.0000.4900.0360.0060.0340.0460.0000.0000.0560.0730.0570.045
px_width-0.0120.0510.0670.041-0.0070.036-0.015-0.0600.0560.4901.000-0.025-0.0220.0130.0540.0000.0000.0000.0000.0530.058
ram-0.044-0.032-0.001-0.048-0.0080.0180.028-0.041-0.0460.036-0.0251.0000.0240.022-0.0050.0550.0000.0000.0560.0730.007
sc_h-0.012-0.054-0.0300.046-0.009-0.025-0.024-0.0360.0200.006-0.0220.0241.0000.4560.0250.0770.0160.0000.0350.0000.000
sc_w0.007-0.033-0.0180.0120.023-0.0110.025-0.000-0.0030.0340.0130.0220.4561.0000.0680.0340.0400.0000.0000.0680.016
talk_time0.0310.015-0.082-0.0490.0240.024-0.022-0.003-0.0380.0460.054-0.0050.0250.0681.0000.0530.0000.0000.0000.0000.057
blue0.0000.0620.0000.0280.0000.0260.0460.0370.0920.0000.0000.0550.0770.0340.0531.0000.0000.0000.0000.0490.000
dual_sim0.0950.0000.0000.0000.0000.0440.0000.0000.0770.0000.0000.0000.0160.0400.0000.0001.0000.0000.0000.0050.000
four_g0.0000.0000.0000.0000.0000.0000.0060.0000.0000.0560.0000.0000.0000.0000.0000.0000.0001.0000.5510.0000.011
three_g0.0000.0000.0220.0170.0000.0000.0910.0000.0000.0730.0000.0560.0350.0000.0000.0000.0000.5511.0000.0000.000
touch_screen0.0000.0000.0290.0000.0000.0290.0870.0360.0000.0570.0530.0730.0000.0680.0000.0490.0050.0000.0001.0000.000
wifi0.0000.0260.0000.0090.0000.0650.0900.0000.0000.0450.0580.0070.0000.0160.0570.0000.0000.0110.0000.0001.000

Missing values

2023-03-27T20:09:45.834130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-27T20:09:46.162016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idbattery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifi
01104311.8114050.1193316226141234761272010
1284110.5141610.81915127468573895607100
23180712.8010270.918634127013662396171010011
34154600.51181250.596820295175238931007110
45143401.40111490.510861874981017731587101
56146412.9151500.81988956993935061073111
67171802.4010471.01562312831374387314210000
7883302.4100620.8111121312188014957218011
89111112.9191250.6101519556876348511910110
910152000.5010250.5171320521009651605101
idbattery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifi
990991180701.2040370.81621112469322741719111
991992179712.6040420.6174320571169335916618111
992993189500.5101620.9992010191698256310813101
99399456712.71141560.41658175551290336767111
99499593611.4100460.8139202658866848512111
995996170011.9001540.5170717644913212114815110
99699760901.8100130.9186421152163219338119011
997998118501.401180.58011247782512235014100
998999153310.5100500.417121238832250915116010
9991000127010.5041350.11406194576082828923101